Maharashtra District Geographical Analysis

import pandas as pd
import seaborn as sns
import numpy as np
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
%matplotlib inline 
from datetime import date

!pip install plotly
import plotly.express as px
import plotly.graph_objects as go

Requirement already satisfied: plotly in c:\users\anshika\anaconda3\lib\site-packages (5.24.1)
Requirement already satisfied: tenacity>=6.2.0 in c:\users\anshika\anaconda3\lib\site-packages (from plotly) (8.2.3)
Requirement already satisfied: packaging in c:\users\anshika\anaconda3\lib\site-packages (from plotly) (24.1)

data = pd.read_csv('maharashtra-districts.csv', encoding= 'latin-1')
data.head()

	District Name	District Code	Administrative Division	Headquarters	Number of Talukas	Area (in sq. km)	Population (Census 2011)	Population Density (per sq. km)	Sex Ratio	Literacy Rate (%)	Urban Population (%)	Formation Date	Geographical Coordinates (Latitude and Longitude)	Major River(s)	Major Crop(s)	Key Industries/Economy	Tourist Attractions
0	Pune	PU	Pune Division	Pune	14	15643	9429408	603	915	86.15	60.5	1 May 1960	18.52° N, 73.85° E	Bhima, Mula, Mutha, Indrayani	Sugarcane, Jowar, Bajra, Grapes, Onions	IT & ITeS, Automotive, Manufacturing, Educatio...	Shaniwar Wada, Aga Khan Palace, Sinhagad Fort,...
1	Satara	ST	Pune Division	Satara	11	10480	3003741	287	988	82.87	21.9	1 May 1960	17.68° N, 74.01° E	Krishna, Koyna, Venna	Sugarcane, Jowar, Soybean, Turmeric	Agriculture, Wind Power, Sugar Factories, Tourism	Kaas Plateau, Mahabaleshwar, Panchgani, Thoseg...
2	Sangli	SN	Pune Division	Sangli	10	8572	2822143	329	966	81.48	25.5	1 May 1960	16.85° N, 74.58° E	Krishna, Warana	Sugarcane, Grapes, Turmeric, Jowar	Sugar Production, Turmeric Processing, Textile...	Sagareshwar Wildlife Sanctuary, Chandoli Natio...
3	Solapur	SO	Pune Division	Solapur	11	14895	4317756	290	938	77.02	32.4	1 May 1960	17.68° N, 75.90° E	Bhima, Sina, Man	Jowar, Sugarcane, Pomegranate, Pulses	Textiles (Chaddars), Sugar Factories, Beedi In...	Siddheshwar Temple, Akkalkot Swami Samarth Mah...
4	Kolhapur	KO	Pune Division	Kolhapur	12	7685	3876001	504	957	81.51	31.7	1 May 1960	16.70° N, 74.24° E	Panchganga, Krishna, Dudhganga	Sugarcane, Rice, Soybean, Jaggery	Sugar Mills, Foundries, Textiles, Kolhapuri Ch...	Mahalakshmi Temple, Panhala Fort, Jyotiba Temp...

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36 entries, 0 to 35
Data columns (total 17 columns):
 #   Column                                             Non-Null Count  Dtype  
---  ------                                             --------------  -----  
 0   District Name                                      36 non-null     object 
 1   District Code                                      36 non-null     object 
 2   Administrative Division                            36 non-null     object 
 3   Headquarters                                       36 non-null     object 
 4   Number of Talukas                                  36 non-null     int64  
 5   Area (in sq. km)                                   36 non-null     int64  
 6   Population (Census 2011)                           36 non-null     int64  
 7   Population Density (per sq. km)                    36 non-null     int64  
 8   Sex Ratio                                          36 non-null     int64  
 9   Literacy Rate (%)                                  36 non-null     float64
 10  Urban Population (%)                               36 non-null     float64
 11  Formation Date                                     36 non-null     object 
 12  Geographical Coordinates (Latitude and Longitude)  36 non-null     object 
 13  Major River(s)                                     36 non-null     object 
 14  Major Crop(s)                                      34 non-null     object 
 15  Key Industries/Economy                             36 non-null     object 
 16  Tourist Attractions                                36 non-null     object 
dtypes: float64(2), int64(5), object(10)
memory usage: 4.9+ KB

data.describe()

	Number of Talukas	Area (in sq. km)	Population (Census 2011)	Population Density (per sq. km)	Sex Ratio	Literacy Rate (%)	Urban Population (%)
count	36.000000	36.000000	3.600000e+01	36.000000	36.000000	36.000000	36.000000
mean	9.944444	8560.666667	3.119993e+06	1436.944444	947.333333	80.864167	33.916667
std	3.970926	4095.966971	2.155514e+06	4569.022377	46.746734	5.596462	22.232814
min	0.000000	157.000000	8.496510e+05	74.000000	832.000000	64.380000	11.000000
25%	7.750000	5614.750000	1.655256e+06	240.750000	929.500000	77.200000	19.450000
50%	9.500000	7701.500000	2.610229e+06	286.000000	944.500000	81.910000	26.350000
75%	14.000000	10880.500000	3.744962e+06	366.500000	959.500000	84.635000	37.550000
max	16.000000	17048.000000	9.429408e+06	20980.000000	1122.000000	89.910000	100.000000

We see that data is mostly normally distributed in Number of Talukas, Sex Ratio, Literacy Rate as well as to some extent in Urban Population. Population Density and Area via intitutively as well as through data show us the skewness. There are some districts which have 100% urban population. 50% of data is below 81.91 % literacy rate. Total there are 36 unique states.

Q.1 Examining which district has maximum no. of Talukas?

data[data['Number of Talukas'] == max(data['Number of Talukas'])]['District Name']

24      Nanded
33    Yavatmal
Name: District Name, dtype: object

Probably large rural population and large area might be the reason for these districts to have such division of Talukas. However it should not be emphasized enough as there might be other factors in such no. of Talukas.

Q.2 What's the relation between Literacy Rate and Sex Ratio?

sns.lmplot(x='Sex Ratio',y= 'Literacy Rate (%)', data= data)
plt.show()

data['Sex Ratio'].corr(data['Literacy Rate (%)'])

-0.1329962817499564

We see that in this interesting case, they roughly have a negative relation, although their Pearson correlation is telling us that there is very weak relationship and there might be other for these two variables in influencing their significance.

from matplotlib.ticker import FuncFormatter
def converter(x,pos):
    return f'{x/1e6:.1f}M'
        
df_sorted= data.sort_values('Population (Census 2011)', ascending= False)
order_list = df_sorted['District Name']


graph= sns.barplot(x='District Name', y='Population (Census 2011)', hue= 'District Name', legend= False,
                   order= order_list, palette='summer', data= data)
graph.yaxis.set_major_formatter(FuncFormatter(converter))
plt.xticks(rotation='vertical')
plt.title('Maharashtra Districts Population as per Census 2011')
plt.show()

Pune and Mumbai Suburban(not Mumbai City) accounts for most of the population as per census 2011. There is very large difference in terms of different population of the states and this totally aligns with the geography of the Maharashtra, where some parts face droughts and other problems, making it difficult to live accessibly.

Q.3 Calculating the Sex Ratio of different states and how they are performing in terms of their comparsion with mean sex ratio of the maharashtra.

state_avg = round(data['Sex Ratio'].mean(),2)
state_avg

947.33

sns.barplot(x='District Name', y='Sex Ratio', hue='District Name', legend= False, 
            palette= 'mako', data= data, order= order_list)
plt.axhline(state_avg, color='red', label='State_avg')
plt.ylim(min(data['Sex Ratio']) -100, max(data['Sex Ratio']) +100)
plt.xticks(rotation= 'vertical')
plt.legend()
plt.title('Comparing Sex Ratio of districts with the state average')
plt.show()

Clearly high population does not result in high sex ratio. Even small districts like Nandurbar performed very well in managing their sex ratio. Mumbai City being the urban city has the lowest sex ratio. This graph shows us different perspectives and parameters should be taken into account while understanding sex ratio of a place. Probably, smaller districts models can be looked upon while initating policies in regards to this topic.

data.head(1)

	District Name	District Code	Administrative Division	Headquarters	Number of Talukas	Area (in sq. km)	Population (Census 2011)	Population Density (per sq. km)	Sex Ratio	Literacy Rate (%)	Urban Population (%)	Formation Date	Major River(s)	Major Crop(s)	Key Industries/Economy	Tourist Attractions	Latitude	longitude
0	Pune	PU	Pune Division	Pune	14	15643	9429408	603	915	86.15	60.5	1 May 1960	Bhima, Mula, Mutha, Indrayani	Sugarcane, Jowar, Bajra, Grapes, Onions	IT & ITeS, Automotive, Manufacturing, Educatio...	Shaniwar Wada, Aga Khan Palace, Sinhagad Fort,...	18.52	73.85

Q.4 Understanding literacy rate in rural population of the maharashtra.

copied_data = data.copy()
Urban_pop = (copied_data['Urban Population (%)']/100)*copied_data['Population (Census 2011)'] 
copied_data['Rural_pop'] = copied_data['Population (Census 2011)'] - Urban_pop
copied_data.sort_values('Rural_pop', ascending= False).reset_index(drop=True)
copied_data.head(2)         

	District Name	District Code	Administrative Division	Headquarters	Number of Talukas	Area (in sq. km)	Population (Census 2011)	Population Density (per sq. km)	Sex Ratio	Literacy Rate (%)	Urban Population (%)	Formation Date	Geographical Coordinates (Latitude and Longitude)	Major River(s)	Major Crop(s)	Key Industries/Economy	Tourist Attractions	Rural_pop
0	Pune	PU	Pune Division	Pune	14	15643	9429408	603	915	86.15	60.5	1 May 1960	18.52° N, 73.85° E	Bhima, Mula, Mutha, Indrayani	Sugarcane, Jowar, Bajra, Grapes, Onions	IT & ITeS, Automotive, Manufacturing, Educatio...	Shaniwar Wada, Aga Khan Palace, Sinhagad Fort,...	3724616.160
1	Satara	ST	Pune Division	Satara	11	10480	3003741	287	988	82.87	21.9	1 May 1960	17.68° N, 74.01° E	Krishna, Koyna, Venna	Sugarcane, Jowar, Soybean, Turmeric	Agriculture, Wind Power, Sugar Factories, Tourism	Kaas Plateau, Mahabaleshwar, Panchgani, Thoseg...	2345921.721

copied_data['Literacy_Category'] = pd.cut(df['Literacy Rate (%)'], bins=3, labels=['Low', 'Medium', 'High'])
plot= sns.kdeplot(x='Rural_pop', hue='Literacy_Category', data=copied_data)
plot.xaxis.set_major_formatter(FuncFormatter(converter))
plt.show()

We see their is more or less normalization towards a rural population of an average 1 million. Although some districts as an outlier has also seen 4 million of rural population, that too in medium literacy rate. High literacy rate generally implying high density and low density being low literacy rate. This might reflect in terms of the infrastructure building and making social policies more accessible in easy terrain or in high density rural population.

Q.5 Analysing density levels of the Urban Population.

labels = [ 'Low Density', 'Medium Density', 'High Density']
copied_data['binned_data'] = pd.qcut(copied_data['Population Density (per sq. km)'], q=3, labels=labels)
grouped_data = copied_data.groupby('binned_data', observed=True)['Urban Population (%)'].mean()
grouped_data

binned_data
Low Density       21.341667
Medium Density    25.741667
High Density      54.666667
Name: Urban Population (%), dtype: float64

sns.barplot(x='binned_data', y='Urban Population (%)', data=copied_data, hue= 'binned_data', legend=False, palette= 'viridis', errorbar=None)
plt.title('Urban Population in different density')
plt.show()

As initutively and with images of Mumbai density stats in mind, we can clearly see high density regions mostly have high urban population. This is directly linked to the level of urbanization and topography.

Q.6 Extracting information about what are the districts in the state producing different Top Crop(s).

data.rename(columns= {'Geographical Coordinates (Latitude and Longitude)': 'Coordinates'}, inplace =True)

data['Latitude'] = data['Coordinates'].str.split(', ').str[0].str.strip('° N')
data['longitude'] = data['Coordinates'].str.split(', ').str[1].str.strip('° E')
data.drop(columns= 'Coordinates', inplace=True)
data.head()

	District Name	District Code	Administrative Division	Headquarters	Number of Talukas	Area (in sq. km)	Population (Census 2011)	Population Density (per sq. km)	Sex Ratio	Literacy Rate (%)	Urban Population (%)	Formation Date	Major River(s)	Major Crop(s)	Key Industries/Economy	Tourist Attractions	Latitude	longitude
0	Pune	PU	Pune Division	Pune	14	15643	9429408	603	915	86.15	60.5	1 May 1960	Bhima, Mula, Mutha, Indrayani	Sugarcane, Jowar, Bajra, Grapes, Onions	IT & ITeS, Automotive, Manufacturing, Educatio...	Shaniwar Wada, Aga Khan Palace, Sinhagad Fort,...	18.52	73.85
1	Satara	ST	Pune Division	Satara	11	10480	3003741	287	988	82.87	21.9	1 May 1960	Krishna, Koyna, Venna	Sugarcane, Jowar, Soybean, Turmeric	Agriculture, Wind Power, Sugar Factories, Tourism	Kaas Plateau, Mahabaleshwar, Panchgani, Thoseg...	17.68	74.01
2	Sangli	SN	Pune Division	Sangli	10	8572	2822143	329	966	81.48	25.5	1 May 1960	Krishna, Warana	Sugarcane, Grapes, Turmeric, Jowar	Sugar Production, Turmeric Processing, Textile...	Sagareshwar Wildlife Sanctuary, Chandoli Natio...	16.85	74.58
3	Solapur	SO	Pune Division	Solapur	11	14895	4317756	290	938	77.02	32.4	1 May 1960	Bhima, Sina, Man	Jowar, Sugarcane, Pomegranate, Pulses	Textiles (Chaddars), Sugar Factories, Beedi In...	Siddheshwar Temple, Akkalkot Swami Samarth Mah...	17.68	75.90
4	Kolhapur	KO	Pune Division	Kolhapur	12	7685	3876001	504	957	81.51	31.7	1 May 1960	Panchganga, Krishna, Dudhganga	Sugarcane, Rice, Soybean, Jaggery	Sugar Mills, Foundries, Textiles, Kolhapuri Ch...	Mahalakshmi Temple, Panhala Fort, Jyotiba Temp...	16.70	74.24

data['Latitude'] = pd.to_numeric(data['Latitude'])
data['longitude'] = pd.to_numeric(data['longitude'])

data['Major Crop(s)'].unique()

array(['Sugarcane, Jowar, Bajra, Grapes, Onions',
       'Sugarcane, Jowar, Soybean, Turmeric',
       'Sugarcane, Grapes, Turmeric, Jowar',
       'Jowar, Sugarcane, Pomegranate, Pulses',
       'Sugarcane, Rice, Soybean, Jaggery', nan,
       'Rice, Vegetables, Fruits', 'Rice, Chickoo, Coconut',
       'Rice, Mango, Cashew Nut, Coconut',
       'Alphonso Mango, Rice, Cashew Nut, Coconut',
       'Alphonso Mango, Cashew Nut, Coconut, Kokum, Rice',
       'Grapes, Onions, Pomegranate, Sugarcane',
       'Sugarcane, Jowar, Bajra, Pulses',
       'Cotton, Jowar, Chilli, Groundnut',
       'Banana, Cotton, Jowar, Pulses', 'Jowar, Cotton, Chilli, Maize',
       'Cotton, Maize, Jowar, Bajra',
       'Sweet Orange (Mosambi), Cotton, Jowar',
       'Cotton, Sugarcane, Jowar, Bajra',
       'Soybean, Pulses (Tur), Grapes, Jowar',
       'Jowar, Soybean, Pulses, Sugarcane',
       'Cotton, Jowar, Turmeric, Soybean', 'Jowar, Cotton, Soybean',
       'Cotton, Soybean, Jowar, Banana', 'Oranges, Cotton, Soybean, Rice',
       'Cotton, Soybean, Pulses (Tur)', 'Rice, Jowar, Pulses',
       'Rice, Pulses, Linseed', 'Cotton, Rice, Soybean, Pulses',
       'Rice, Tendu Leaves, Bamboo, Mahua',
       'Cotton, Soybean, Tur (Pigeon Pea), Oranges',
       'Cotton, Jowar, Soybean, Pulses', 'Cotton, Soybean, Pulses',
       'Cotton, Jowar, Maize, Soybean', 'Soybean, Cotton, Tur, Jowar'],
      dtype=object)

ser1= data['Major Crop(s)'].str.split(', ').explode() # Extracting the unique crops and making a dataframe of theirs
expanded_df = data.loc[ser1.index].copy() #Setting the new dataframe index based on the original data index
expanded_df['Major Crop(s)'] = ser1.values
expanded_df['Major Crop(s)'] = expanded_df['Major Crop(s)'].str.strip()
unique_crop= expanded_df['Major Crop(s)'].value_counts()


sns.barplot(x=unique_crop.values, y= unique_crop.index, palette= 'cool_d', hue=unique_crop.values, legend=False)
plt.title('Major Crops Produced in the state')
plt.show()

Jowar, representing the climate condition as well as our stat of Maharashtra being the top most producer of this crop, can be seen clearly. However we see variety of crops are grown in the state. Some are also very famous to the state like oranges and alphonso mangoes.

Top_4_crops= unique_crop.head(4).index.tolist()
Top_4_crops

['Jowar', 'Cotton', 'Soybean', 'Rice']

We find the Jowar, Cotton, Soybean and Rice being the top most crops.

import json
with open('Maharashtra.geojson', 'r') as f:
    geojson = json.load(f)

for crop in Top_4_crops:
    df = data[data['Major Crop(s)'].str.contains(crop, case=False, na= False)]
    unique_districts = df['District Name'].unique().tolist()
    z_values = list(range(len(unique_districts)))


# Creating the choropleth map
    fig = go.Figure(go.Choropleth(
        geojson=geojson,
        locations=unique_districts,
        z=z_values,
        colorscale=px.colors.qualitative.Plotly,
        locationmode='geojson-id',
        featureidkey='properties.Dist_Name', 
        marker_opacity=0.5,
        marker_line_width=1,
        marker_line_color='black',
        showscale= False # Hide the color bar
    ))
    
# For map layout
    fig.update_geos(
        visible=True,
        scope='asia',
        center={"lon": 80, "lat": 22},
        projection_scale=10,
        fitbounds='locations'
    )
    fig.update_layout(
        title=f'{crop} producing major districts'
    )
    
    fig.show()
    

Central Maharashtra is the major region in terms of major crop production. Rice cultivation is mostly concentrated in eastern region, sea side specifically. Some districts appear to be most influential like Nanded, Chandrapur, Nagpur etc specializing in different crop production.

Thank You!